System and methods utilizing artificial intelligence algorithms to analyze wearable activity tracker data

ABSTRACT

A system and method are disclosed for monitoring health conditions based on data collected by a wearable device such as an activity tracker or a smart watch. Deep learning algorithms are configured to process an input vector that includes monitored parameter data collected by the wearable device as well as embedding data obtained from health records corresponding to a user account registered to the wearable device. In some embodiments, the input vector can also include social determinants data and/or demographic data. The output of the deep learning algorithms provides classifiers that represent probabilities that the user of the wearable device has an underlying health condition. If any underlying health condition is detected, then the user can be notified directly, via the wearable device or an associated application or technology, or indirectly, via a primary care provider associated with the user.

TECHNICAL FIELD

The application relates to wearable activity trackers. Morespecifically, the application is directed to artificial intelligencealgorithms configured to analyze wearable activity tracker data toassist with health monitoring.

BACKGROUND

Wearable health monitoring systems such as the features built intoconsumer devices like a Fitbit® device or an Apple® Watch, among others,or clinical devices like network connected blood glucose monitors arebecoming more common. Consumers are tracking their steps (e.g.,pedometer), heart rate (e.g., resting hear rate, peak heart rate, heartrate variability, etc.), oxygen level (e.g., VO2 Max), activity levels(e.g., calories, minutes of exercise, etc.), and the like using thesedevices, typically to encourage a more active and healthy lifestyle.This data generates thousands or millions of data points for a consumer,and some of this information can be highly correlated to the presence ofcertain diseases, oftentimes undiagnosed. However, this data istypically not shared with a patient's primary care physician or used inany clinical setting for medical diagnosis.

Some of the consumer companies have developed application programminginterfaces (APIs) that allow this data to be shared with third parties,such as a clinician or a company offering health-based incentives suchas discounts for those individuals that meet certain goals. In somecases, certain parameters like heart rate or VO2 max can be tracked overtime and analyzed by artificial intelligence algorithms to attempt todetect certain health conditions. However, such algorithms are typicallylimited as they are not tailored to a specific user's physiology. Theeffectiveness of the artificial intelligence algorithm might not besufficient for a user to rely on the output of said algorithm.Therefore, there is a need to improve the algorithms directed to usethis wearable activity tracker data.

SUMMARY

A system and method for utilizing wearable device data to perform healthmonitoring are disclosed. Deep learning techniques are utilized toanalyze monitored parameter data collected by a wearable device inconjunction with additional embedding data derived from health recordsor other sources. In particular, the techniques and systems describedherein can be utilized by insurance providers or other sources of healthrecord information to supplement the monitored parameter data collectedby the wearable device.

In accordance with one aspect of the present disclosure, a system forperforming health monitoring is disclosed. The system includes a serverdevice including a memory and one or more processors. The one or moreprocessors are configured to: receive monitored parameter data from awearable device; obtain embedding data corresponding to a userregistered to the wearable device; process the monitored parameter dataand the embedding data to generate an input vector; process the inputvector by an artificial intelligence algorithm to generate an outputvector; and generate a notification message based on the output vector.

In some embodiments, the wearable device is an activity tracker. Themonitored parameter data includes data points related to one or more of:a heart rate, an oxygen level, an activity level including at least oneof a number of steps, a number of flights climbed, or a duration ofexercise, or a number of calories burned. In an embodiment, themonitored parameter data further includes information logged by a usermanually, where the information can include, but is not limited to, oneof a height or a weight of the user.

In some embodiments, obtaining the embedding data comprises processinghealth records for a user via a natural language processing algorithm.The health records correspond to a registered user of the wearabledevice.

In some embodiments, the health records are stored in a database andcomprise at least one of: claims records received from a health careprovider, prescription records received from a pharmacy, or laboratoryresults received from a laboratory or other health care provider.

In some embodiments, the input vector data further includes socialdeterminants data including one or more of: economic information,neighborhood information, education information, nutritionalinformation, or environmental information. In some embodiments, theinput vector data further includes demographic data including one ormore of: age information, gender information, neighborhood typeinformation (e.g., urban, suburban, rural, etc.), family sizeinformation, or employment indicator information.

In some embodiments, the artificial intelligence algorithm comprises oneor more of: a multi-layer perceptron (MLP) algorithm, a convolutionneural network (CNN), or a recurrent neural network (RNN).

In some embodiments, the notification message is transmitted to one ofthe wearable device or a mobile device associated with the wearabledevice to provide a user of the wearable device with a suggested action.In an embodiment, the suggested action includes information tofacilitate scheduling an appointment with a healthcare provider.

In some embodiments, the notification message is transmitted to ahealthcare provider to suggest treatment options that may be applicableto a patient.

In another aspect of the present disclosure, a method is disclosed forperforming health monitoring. The method includes the steps of:receiving monitored parameter data from a wearable device, obtainingembedding data corresponding to a user registered to the wearabledevice, processing the monitored parameter data and the embedding datato generate an input vector, processing the input vector by anartificial intelligence algorithm to generate an output vector, andgenerating a notification message based on the output vector.

In yet another aspect of the present disclosure, a wearable device isdisclosed. The wearable device includes a memory for storing data pointsassociated with one or more monitored parameters, a transceiver forcommunicating with a server device over a network, and at least oneprocessor. The at least one processor is configured to: sample one ormore sensors to generate data points for each of the one or moremonitored parameters, store the data points in the memory, transmit atleast a portion of the data points to the server device, and receive anotification message from the server device. The notification messageincludes a suggested action identified based on the output of anartificial intelligence algorithm configured to process an input vectorthat includes the at least a portion of the data points for the one ormore monitored parameters and embedding data corresponding to thewearable device.

In some embodiments, the wearable device comprises a wearable activitytracker.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a system for monitoring health conditions, inaccordance with some embodiments.

FIG. 1B illustrates a system for monitoring health conditions, inaccordance with other embodiments.

FIGS. 2A & 2B illustrate a wearable device, in accordance with someembodiments.

FIG. 3 is an artificial intelligence algorithm, in accordance with someembodiments.

FIG. 4 illustrates a convolution neural network (CNN), in accordancewith some embodiments.

FIG. 5 is a flow diagram of a method for identifying undetected medicalconditions based on monitored parameter data collected by a wearabledevice, in accordance with some embodiments.

FIG. 6 is a conceptual illustration of a health monitoring service foridentifying medical conditions based on monitored parameter datacollected by a wearable device, in accordance with some embodiments.

FIG. 7 illustrates an exemplary computer system, in accordance with someembodiments.

DETAILED DESCRIPTION

Artificial Intelligence (AI) and Deep Learning Neural Networks (DLNN)can be utilized to assist in health monitoring techniques. A wearabledevice, such as a wearable activity tracker, oximeter, blood glucosemonitor, or the like, can provide one or more monitored parameters to aserver device. The monitored parameters can include, but are not limitedto, a heart rate, heart rate variability, an electro cardiogram (ECG), ablood oxygen level, a duration of exercise, a number of steps, analtitude, exposure to environmental parameters (e.g., audio signals, airquality, temperature, etc.), menstruation cycles (e.g., basal bodytemperature, etc.), sleep cycles, acceleration (e.g., fall detection),and the like. The parameters can be logged automatically via sensorinput or manually via user input, or any combination thereof. In someembodiments, the wearable device can be paired with other devices (e.g.,blood glucose monitor, etc.) to collect parameter data. The monitoredparameters can provide data points collected periodically, such as everysecond, minute, hour, or day. Different parameters can be collected atdifferent frequencies. For example, a heart rate may be collected everyminute whereas a heart rate variability can be collected daily (e.g., asderived from a plurality of heart rate data points collected throughouta 24 hour period).

A user registers the wearable device with a user account. The useraccount can be associated with additional data that can be used tosupplement the monitored parameter data from the wearable device. Forexample, the additional data can include claims data for an insuredperson that is maintained by an insurance provider, which can includedata collected from the insured person's primary care physician,clinical labs, pharmacies, or other health providers that submit claimsinformation to the insurance provider for payment and/or processing. Theclaims data provides the insurance provider with additional insight intothe health history of the patient that is useful for augmenting anartificial intelligence algorithm tasked with analyzing the informationfrom the wearable device.

While the wearable device provides the AI algorithm with limited healthinformation in a real-time or near real-time basis, the data istypically limited in scope and only provides insight into a small numberof parameters related to a user's health. In contrast, the claimsinformation may provide much more detailed insight into a specificuser's physiology, but is typically collected on a much more limitedbasis (e.g., during once or twice a year appointments, when a personfills a prescription, when labs are run, etc.). By combining this datato provide a broad range of information to an AI algorithm, much moreaccurate assessments can be made by the AI algorithm, providing a userwith more insight into potential health concerns before the person mightotherwise be aware of the symptoms of such conditions. Experimentaltests have seen 5-10% increases in accuracy of AI algorithms trainedbased on both monitored parameter data from a wearable device as well asadditional embedding data from health records.

FIG. 1A illustrates a system 100 for monitoring health conditions, inaccordance with some embodiments. As depicted in FIG. 1A, the system 100includes a server device 110 connected to a network 150. The serverdevice 110 comprises a memory and one or more processors configured toexecute instructions. In an embodiment, the server device 110 is a bladeserver included in a chassis on a rack of a data center. In anotherembodiment, the server device 110 is a virtual machine that is run inthe cloud. Although the components shown in the server device 110 can beexecuted by a single processor, in other embodiments, the server device110 can refer to two or more server devices, each component included inthe server device 110 executed by a particular one or more of the two ormore server devices. For example, in an embodiment, the AI engine 114can be executed on a second server device that is implemented in apublic cloud such as Amazon® Web Services or Microsoft® Azure. The API112, embedding engine 116, and/or notification engine 118 can beimplemented on a private server device hosted in a data center operatedby an insurance provider and can be configured to communicate with theAI engine 114 over a network. It will be understood that reference to asingle server device 110 can include reference to multiple serverdevices.

In some embodiments, the server device 110 includes an applicationprogramming interface (API) 112, an artificial intelligence (AI) engine114, an embedding engine 116, and a notification engine 118. The API 112implements a set of algorithms to process API calls from a client. In anembodiment, the API 112 is configured to receive data packets thatinclude the API calls from a wearable device 180. The API calls caninclude monitored parameter data collected by the wearable device 180.As used herein, the term “monitored parameter data” refers to one ormore data points (e.g., samples) of each of one or more monitoredparameters such as, but not limited to, a heart rate, heart ratevariability, and ECG, an oxygen level, an activity level including atleast one of a number of steps, a number of flights climbed, or aduration of exercise, a number of calories burned, a basal bodytemperature, a sleep duration, a blood glucose level, or the like. Forexample, the wearable device 180 can include light sensors that, whenplaced proximate a user's skin, are used to monitor a user's heart rateor blood oxygen level. As another example, the wearable device 180 caninclude a temperature sensor placed proximate the user's skin, thatmeasures a surface temperature of the user's body at the location of thetemperature sensor. The wearable device 180 collects one or more datapoints over a period of time such as a day or week, and then thewearable device 180 generates an API call including the data points andtransmits the API call to the server device 110 via the network 150.

In some embodiments, the network 150 is a wide area network (WAN) suchas the Internet. In other embodiments, the network 150 can be a radioaccess network such as a 4^(th) Generation (LTE, 4G) or Next Generation(5G) cellular network. In yet other embodiments, the network 150 can bea local area network (LAN) or a personal area network (PAN). The datapackets can be Internet Protocol (IP) packets. It will be appreciatedthat any network, wired or wireless, can be utilized to transmitmonitored parameter data to the server device 110 using any technicallyfeasible protocols.

In some embodiments, the API 112 pre-processes the monitored parameterdata received from the wearable device 180 and transmits the processedmonitored parameter data to the AI engine 114. For example, the heartrate data can represent a number of data points, and the API 112 can beconfigured to derive average daily heart rate data from the monitoredparameter data. In an embodiment, the API 112 can be configured tofilter the monitored parameter data to select only a subset ofparameters that are applicable for a given AI algorithm. For example,the monitored parameter data can include heart rate data, stepinformation, calorie information, and oxygen level information. Aparticular AI algorithm may only call for heart rate data and oxygenlevel information and, therefore, the API 112 is configured to discardthe step information and calorie information.

In some embodiments, the API 112 is configured to anonymize themonitored parameter data. Information related to a user's health is aprivacy concern and, therefore, the API 112 can take steps to ensure anypersonally identifying information (PII) is removed before the monitoredparameter data is forwarded to the AI engine 114. It will be appreciatedthat the API 112 may need to associate the monitored parameter data witha particular user or device, such as by using an identifier included inthe API call or the header of a data packet to correlate the wearabledevice 180 with a particular user. The identifier can be a useridentifier (ID) that is assigned to the wearable device 180 when theuser registers the wearable device with a health monitoring service, orthe identifier can be a network address, such as a media access control(MAC) address or an IP address of the wearable device 180. In someembodiments, the API 112 can use the identifier to allocate a task IDwith the monitored parameter data such that the AI engine 114 or anyother process that accesses the data cannot directly correlate the datawith a particular user, user account, or user ID. In some cases, themonitored parameter data is also encrypted, either by the wearabledevice 180 or by the API 112.

In other embodiments, the API 112 stores the monitored parameter data ina memory or database, and sends a request message to the AI engine 114that indicates the monitored parameter data is available at the memoryor the database. It will be appreciated that the API 112 facilitates thecollection of monitored parameter data from one or more wearable devices180 associated with one or more distinct users or user accounts andprovides the AI engine 114 with the monitored parameter data forprocessing.

In some embodiments, the AI engine 114 is configured to process inputswith one or more AI algorithms. The AI algorithms can include, but arenot limited to, a multi-layer perceptron (MLP) algorithm, a convolutionneural network (CNN), or a recurrent neural network (RNN). The AIalgorithm uses the monitored parameter data to generate outputs thatrepresent predictions related to the health of a user. For example, aCNN may be utilized to process an input vector including a number ofdata points that represent a heart rate variability for a user over anumber of days (e.g., 7 days, 30 days, etc.). The heart rate variabilitydata can be shown to be indicative of certain underlying healthconditions such as hypertension, hyperlipidemia, sleep apnea, diabetes,and cardiovascular disease. The use of certain monitored parameter datacollected from a wearable device and analyzed by deep learning modelsmay be useful in helping to diagnose people with undetected conditions,but the accuracy of such models is not perfect because each individualhas a unique physiology and environment. The heart rate of a younghealthy individual with low body fat when compared against the heartrate of an older obese individual can be very different, even though theyoung person may be diabetic and the older person may not have anydetected condition. Therefore, the AI algorithms can be improved byaugmenting the monitored parameter data collected by the wearable devicewith additional information related to the user.

In an embodiment, the AI engine 114, upon receiving the monitoredparameter data from the API 112, generates a request that is transmittedto the embedding engine 116. The request can include an identifierassociated with the wearable device 180. The embedding engine 116 isconfigured to collect health records or other information from one ormore data stores such as a database 160. For example, a health insuranceprovider can receive claims data from a variety of health careproviders. The claims data is stored in a database 160 and used toprocess insurance claims. However, the embedding engine 116 can querythe database 160 for any health records related to a user using theidentifier included in the request. Thus, the embedding engine 116collects additional information from one or more sources that is notavailable to the wearable device 180.

In some embodiments, the embedding engine 116 is configured to collectthe additional information that is combined with the monitored parameterdata from the wearable device 180 to increase the information providedas an input to the AI algorithm. In one example, a health insuranceprovider receives data related to a user's health as well as generaldemographic information and/or social determinants information. The datacan include claims records received from a health provider, prescriptionrecords received from a pharmacy, or laboratory results received from alaboratory or other health care provider. The claims information maycontain information about specific medical procedures undergone by theuser in the past. The laboratory results can contain detailedinformation related to a user's comprehensive metabolic panels, forexample. The prescription records can include information about thetypes of medications taken by a user. All of this information canprovide a more comprehensive picture of the user's health condition thancould be provided by the monitored parameter data alone.

In an embodiment, the embedding engine 116 is configured to processhealth records for a user via a natural language processing algorithm.Again, the health records correspond to a registered user of thewearable device. It will be appreciated that the health records that areretrieved from the database 160 can have a variety of formats. Forexample, a claims record may include a set of fields, each fieldincluding different information such as a member identifier, insuranceproduct information, medical codes (e.g., ICD9 diagnostic codes), andthe like. The job of the embedding engine 116 is to take a number ofdifferent natural language or standardized format health records andgenerate a vector of dimensionless features (e.g., 100 features) thatindicates a user's health history over a specified time period. Forexample, a particular dimension can represent a particular combinationof medical codes found throughout the user's health records. Thisfeature vector, generated by a natural language processing algorithmsuch as Word2vec described in Mikolov et al., “Efficient Estimation ofWord Representations in Vector Space,” ICLR Workshop Papers, 2013, whichis herein incorporated in its entirety, or the like, represents theadditional data that is combined with the monitored parameter data.

For example, a member's health history includes a sequence of medicalcodes including ICD (International Statistical Classification ofDiseases) 9/10 diagnostic codes, CPT (Current Procedural Terminology)procedure codes, GPI (Generic Product Identifier) drug codes, and LOINC(Logical Observation Identifiers Names and Codes) lab codes. Theembedding engine 116 includes a 100-dimensional lookup matrix that istrained based on health histories of millions of members collected overa number of years. The health history of a particular member can beparsed to extract the codes set forth above, to generate a list of codesincluded in that member's health history. Each code is then processed bythe lookup matrix to generate a corresponding 100-dimensional vector andthe average of the vectors for the set of codes generated from themember's health history is provided as the embedding data.

In some embodiments, the embedding data is combined with additional datasuch as demographic data (e.g., age, race, gender, etc.) or socialdeterminants data, described in more detail below. When combined withthe monitored parameter data, this information provides a morecomprehensive view of the user's health that can be processed by the AIalgorithm.

The AI engine 114 processes the input vector via one or more AIalgorithms. In some embodiments, the AI engine 114 can generate separateinput vectors for each AI algorithm, according to the requirements ofthe AI algorithm. For example, one type of algorithm can be better fordiagnosing a medical condition such as hypertension while a differenttype of algorithm can be better for diagnosing a medical condition suchas sleep apnea. The output vector from each of the one or more AIalgorithms can represent classifiers associated with different medicalconditions. A classifier can be a scalar value between 0 and 1 thatrepresents a probability that the user may have the underlying healthcondition associated with that classifier.

Once the output vector(s) are generated, the AI engine 114 may compareeach classifier to a threshold value. If the classifier is above athreshold value, then the user may have a high likelihood of having theunderlying health condition. In such cases, the AI engine 114 cantransmit a request to the notification engine to notify the user aboutthe detected health condition.

In an embodiment, the notification engine 118 can generate anotification message that is transmitted to the wearable device 180 toprovide a user of the wearable device with a suggested action. Forexample, the suggested action can include information to facilitatescheduling an appointment with a healthcare provider. In anotherembodiment, the notification message is transmitted to a healthcareprovider to suggest treatment options that may be applicable to apatient (e.g., the user).

FIG. 1B illustrates a system 100 for monitoring health conditions, inaccordance with other embodiments. As depicted in FIG. 1B, the wearabledevice 180 includes and application 192 configured to collect datapoints on one or more parameters monitored by the wearable device 180.In addition, the wearable device 180 also includes the AI engine 194,which is similar to AI engine 114 but is executed locally on thewearable device 180. Rather than transmitting the parameter datacollected by the wearable device 180 to the sever device 110, theapplication 192 can request the server device 110 to transmit embeddingdata generated by the embedding engine 116 to the application 192, whichcombines the embedding data with the monitored parameter data stored onthe wearable device 180. The combined data is then processed by the AIengine 114 to generate the output vector(s) as described above.

It will be appreciated that the system 190 may be more secure than thesystem 100 as the monitored parameter data is not transmitted over thenetwork 150, and the embedding data transmitted over the network 150 ismerely a vector that represents a user's health history and is noteasily translated into the original health history information stored inthe database 160. However, the system 190 may require more computingcapacity in the wearable device 180 in order to execute the AIalgorithm(s).

In some embodiments, the functionality of the wearable device 180 can besplit between a wearable activity tracker with limited processingcapacity and a mobile device, such as a cellular phone or tabletcomputer. The mobile device can be connected to the network 150 and thewearable activity tracker can be connected to the mobile device. Anapplication on the mobile device collects monitored parameter data fromthe wearable activity tracker and transmits the monitored parameter datato the server device 110 to be processed by the AI engine 114, as insystem 100, or receives embedding data from the server device 110 to beprocessed by the AI engine 194, as in system 190. In other embodiments,the mobile device can be used to provide the wearable device 180 with atleast some monitored parameter data. For example, a user can use themobile device to provide manually logged data related to one or moremonitored parameters that are sent to the wearable activity tracker forprocessing. In such embodiments, the mobile device can provideadditional user interfaces for the wearable activity tracker, but thewearable activity tracker maintains the functionality for processing themonitored parameter data.

FIGS. 2A & 2B illustrate a wearable device 200, in accordance with someembodiments. The wearable device 200 is a watch that includes sensorsfor collecting data points on one or more parameters related to thehealth of a user. As shown in FIG. 2A, the wearable device 200 includesa body 202 that houses electronics including a processor, a memory, atransceiver, an antenna, a battery, and the like. The wearable device200 also includes a button 204, a display 210, and a band 212. Thedisplay 210 is a liquid crystal display (LCD), an organic light emittingdisplay (OLED), or the like. The display 210 is configured to displayinformation to a user and can include a touch function that enables theuser to enter information using the wearable device. The display 210 candisplay information such as a time or date, notifications such as alarmsor indications of a text message or phone call, and information relatedto collected information such as a number of steps counted in a dayusing a pedometer function or a current heart rate of the user, amongother information.

As shown in FIG. 2B, the back of the body 202 can include sensors 230for collecting monitored parameter data. For example, the sensors 230can include a light emitting diode that produces a light and a lightsensor that detects lights (e.g., optical pulse sensors). The lightemitted from the light emitting diode can enter the user's skin and thesignal detected by the light sensor can be used to monitor, for example,a user's heart rate or blood oxygen level. The sensors 230 can includeother types of sensors such as capacitive sensors, moisture sensors,image sensors, electrooculogram (EOG) or electroretinogram (ERG)sensors, respiration sensors, airflow sensors, temperature sensors, andthe like. The scope of the present disclosure is not limited to aspecific type of sensor and any sensor capable of collecting any kind ofhealth information about a user is contemplated as being included in thewearable device 200.

As depicted in FIG. 2B, the wearable device 200 includes a processor 240and a transceiver 250. In some embodiments, the processor 240 is asystem on chip (SoC) and is configured to execute one or more processesthat collect monitored parameter data using the sensors 230 and transmitthe monitored parameter data to the server device 110 via thetransceiver. In one embodiment, the wearable device 200 is configured totransmit the monitored parameter data to an access point connected to anetwork using Wi-Fi or some other wireless technology.

It will be appreciated that the wearable device 200 shown in FIGS. 2A &2B is only one exemplary device and that other types of wearable devicesare contemplated as being within the scope of this disclosure. Forexample, the wearable device 200 could be a clinical medical device suchas a blood glucose monitor or a wearable electrocardiogram (ECG)monitor, smart glasses that include augmented reality and/or virtualreality capabilities, smart rings, bracelets, headbands, hearing aids,or any other devices worn by a user that include one or more sensors formonitoring some parameter related to the user. In some embodiments, thewearable device is a smart phone that includes a fitness applicationthat tracks, e.g., an activity level of a user.

FIG. 3 is an artificial intelligence algorithm 300, in accordance withsome embodiments. The AI algorithm 300 is a multi-layer perceptron (MLP)algorithm. The MLP is a neural network that includes a plurality ofartificial neurons arranged in a series of layers. As depicted in FIG.3, the MLP includes an input layer 310, one or more hidden layers 320,and an output layer 330. Each artificial neuron within a layer isconnected to one or more artificial neurons in a subsequent layer, butno artificial neuron in a particular layer is connected to anotherartificial neuron in the particular layer.

An input vector, {x₁, x₂, . . . , x_(j)}, is provided as inputs to theinput layer 310, where each element of the input vector is coupled toone of the artificial neurons in the input layer 310. Each artificialneuron applies a linear transformation and, optionally, a nonlineartransformation to the inputs connected to the artificial neuron. In thelinear transformation, a weight can be applied to each of one or moreinputs attached to the artificial neuron and then the weighted inputscan be summed to produce an intermediate output signal. In the nonlineartransformation, the intermediate output signal can be processed by anactivation function such as a Sigmoid function or a Rectified LinearUnit (ReLU) to produce an activation signal of the artificial neuron.The activation signal can be fanned out to one or more artificialneurons in the subsequent layer.

The input layer 310 is followed by one or more hidden layers 320. Thenumber of artificial neurons in the hidden layer 320 does not have tomatch the number of artificial neurons in the input layer 310. In someembodiments, each hidden layer is fully connected, meaning that eachartificial neuron in the hidden layer receives all of the activationsignals generated by the set of artificial neurons in the previous layer(e.g., the input layer or a preceding hidden layer). In otherembodiments, each artificial neuron in the hidden layer receives only asubset of activation signals generated by the set of artificial neuronsin the previous layer.

The output layer 330 follows the last hidden layer 320 and generates anoutput vector, {y₁, y₂, . . . , y_(k)}. It will be appreciated that thedimension j of the input vector can be independent of the dimension k ofthe output vector. In an embodiment, each element of the output vectorrepresents a probability of a person associated with the input vectorhaving a particular underlying medical condition.

In an embodiment, the input vector is composed of a first portioncorresponding to monitored parameter data from a wearable device 180 anda second portion corresponding to embedding data associated with a userregistered to the wearable device 180. In some embodiments, the inputvector can also include additional information such as demographic dataand/or social determinants data, as discussed in more detail below.

It will be appreciated that the AI algorithm 300 is trained using atraining data set. In an embodiment, a health insurance company collectsdata related to a large number of insured individuals. The data caninclude both health records and monitored parameter data from wearabledevices. The health records provide insight on subpopulations in thenumber of insured individuals that have been diagnosed with one of avariety of medical conditions. The health records can be analyzed tocreate a ground-truth output vector for each individual. The healthrecords and monitored parameter data collected from a wearable deviceregistered to that individual can be used to generate an input vectorthat corresponds to the ground-truth output vector. Pairs of inputvectors and ground-truth output vectors can then be stored in a databasefor a large number of insured individuals to generate the training dataset.

In order to train the AI algorithm 300, various parameters of the AIalgorithm 300 are initialized. In an embodiment, each artificial neuronis associated with a set of weights corresponding to the inputsconnected to the artificial neuron. Each weight can be initialized to arandom or pseudo-random value (e.g., each weight can be set between 0and 1, or −1 and 1). In some embodiments, each artificial neuron canalso be associated with a bias value. Once the parameters of the AIalgorithm 300 are initialized, each input vector in the training dataset is processed by the AI algorithm 300 to generate an output vector.The output vector is compared to the ground-truth output vector for thatindividual based on a cost function, and the parameters of the AIalgorithm 300 are adjusted using known techniques, such as backpropagation with gradient descent, in order to minimize the costfunction. The cost function can be an L1 cost function such as a sum ofabsolute differences (SAD) or an L2 cost function such as a sum ofsquare differences (SSD).

Once the AI algorithm 300 is trained, the AI algorithm 300 can beemployed with new user data. As monitored parameter data is receivedfrom a wearable device 180, the input vector can be generated, includingthe corresponding embedding data, and processed to generate an outputvector. The output vector classifies the probability that the user mayhave one of a plurality of undiagnosed medical conditions.

As described above, the MLP is only one type of AI algorithm 300 thatcan be implemented by the AI engine 114. The MLP is applied to a onedimensional input vector having a number of elements. In contrast, otherAI algorithms 300 can be implemented that analyze a two dimensionalinput vector.

FIG. 4 illustrates a convolution neural network (CNN) 400, in accordancewith some embodiments. A CNN is typically used in applications such asimage classification or image processing. The convolution operationapplies a moving filter, referred to as a convolution kernel, across theinput image to generate a set of feature maps. Each layer of the CNNfurther refines the set of feature maps through subsequent convolutionoperations and downsampling in order to generate a large number offeatures that are significantly reduced in spatial resolution. A fullyconnected layer then combines these features to generate an outputvector. In some embodiments, CNNs can be implemented as U-nets, whichinclude an encoder portion that generates the set of features and adecoder portion which performs deconvolution operations and upsamplingto generate a processed image from the set of features generate by theencoder portion.

As depicted in FIG. 4, an input vector 410 is a two-dimensional matrix.A first portion of the matrix comprises monitored parameter data. Asecond portion of the matrix comprises embedding data. In someembodiments, the matrix can also include demographic data and/or socialdeterminants data. In some embodiments, the input vector is a pluralityof channels, each channel including a two dimensional matrix. A firstchannel includes the monitored parameter data, a second channel includesthe embedding data, and, optionally, a third channel and/or a fourthchannel includes demographic data and social determinants data,respectively.

A first layer 415 of the CNN 400 performs a two dimensional convolutionoperation on the input vector 410 to generate a set of feature maps 420.The number of channels of the feature maps can be more than the numberof channels of the input vector 410. For example, each channel of thefeature maps 420 can correspond to a different convolution kernelapplied to a particular channel of the input vector 410.

A second layer 425 is a downsampling layer that generates a second setof feature maps 430 from the set of feature maps 420. The downsamplingoperation can be implemented by a pooling layer or, less typically, aconvolution operation with a stride greater than one element. A striderefers to moving the convolution filter by more than one element eachtime the convolution filter is applied to the input feature map. Forexample, a stride of 2×2 means that the convolution filter is applied toevery other element of the feature map such that the output feature mapis half the resolution of the input feature map in each dimension. Apooling layer, on the other hand, is applied to a subset of elements(e.g., each 2×2 set of elements) and selects, either, a maximum value inthe subset, a mean value in the subset, or a minimum value in the subsetas the output for a corresponding element in the output feature map. Itwill be appreciated that the number of channels in the feature mapsoutput by a pooling layer is typically the same as the number ofchannels in the feature maps input to the pooling layer. However, wheresubsampling is implemented using convolution operations with stridesgreater than one in either dimension, the number of channels in thefeature maps can increase.

The CNN 400 can include additional layers such as a convolution layer435 and a subsampling layer 445 that generate sets of feature maps 440and 450, respectively. Although not shown explicitly, a large number oflayers can be included in the CNN 400. Examples of deep neural networkscan include more than 50 or 100 layers. Furthermore, although not shownexplicitly, the CNN 400 can also include activation layers, which applyan activation function to each of the elements of the feature maps.Again, examples of common activation functions are ReLU and Sigmoid.

The final layer 455 is a fully connected layer that processed the set offeature maps 450 to generate an output vector 460. In one embodiment,the output vector 460 is a one-dimensional vector that classifies theprobability of an individual to have each of a plurality of undetectedmedical conditions. In other embodiments, the output vector 460 can be atwo-dimensional matrix.

It will be appreciated that the AI algorithm is not limited to the MLPor the CNN implementations described above. In another embodiment, theAI algorithm is a recurrent neural network (RNN). In an embodiment, theRNN is implemented similar to the MLP of FIG. 3, except each neuralnetwork maintains a state h that can affect the activation level of theartificial neuron. Essentially, a recurrent neural network is similar tothe MLP except that the past state of each neuron is not returned to abase state before processing a new input vector. In another embodiment,the RNN is implemented similar to the CNN of FIG. 4, where the fullyconnected layer 455 is replaced with one or more recurrent layers (e.g.,bidirectional layers) that maintain a state h.

Other types of AI algorithms are also within the scope of the presentdisclosure. These algorithms can include residual neural networks,regression algorithms, and the like. The particular type of AI algorithmthat is implemented can depend on the particular application and mayconsider the particular type of monitored parameter data received from awearable device 180.

FIG. 5 is a flow diagram of a method 500 for identifying undetectedmedical conditions based on monitored parameter data collected by awearable device, in accordance with some embodiments. The method 500 canbe performed by a program, custom circuitry, or by a combination ofcustom circuitry and a program. Furthermore, persons of ordinary skillin the art will understand that any system that performs method 500 iswithin the scope and spirit of the embodiments described herein.

At step 502, monitored parameter data is received from a wearabledevice. In an embodiment, a server device 110 receives the monitoredparameter data from a wearable device 180 via a network 150. Themonitored parameter data can include data points (e.g., samples) for oneor more monitored parameters that can include at least one of: a heartrate; an oxygen level, an activity level including at least one of anumber of steps, a number of flights climbed, or a duration of exercise,or a number of calories burned. In some embodiments, the monitoredparameter data is collected automatically using sensors included in thewearable device 180. In some embodiments, the monitored parameter datacan include manual logged data that is manually entered into thewearable device 180 by a user. Manual logged data can includecharacteristics such as a height or weight of a user, exercise activity(e.g., a number of minutes for the duration of exercise, a type ofexercise, etc.), and the like. In some cases, these characteristics canbe automatically logged by the wearable device using sensors orinterfaces between the wearable device and other health or fitnessequipment. For example, a wearable tracker can establish a communicationchannel with a smart scale that enables the user's weight toautomatically be logged by the wearable tracker every time the usersteps on the scale. As another example, the wearable tracker can includeinertial sensors that enable the tracker to determine motion consistentwith exercise activity and log the number of minutes that the activityis performed.

At step 504, embedding data is obtained corresponding to a userregistered to the wearable device. In an embodiment, a user registersthe wearable device with a user account. The user account can correspondto a product or service offered by a service provider such as, forexample, a user account for an insured individual that is enrolled in aninsurance product offered by an insurance provider. In some embodiments,the embedding data is obtained by retrieving health recordscorresponding to the user registered to the wearable device 180.Obtaining the embedding data can include processing one or more healthrecords for a user via a natural language processing algorithm. Theoutput of the natural language processing algorithm can be a featurevector including a number of features (e.g., scalar values).

At step 506, the monitored parameter data and the embedding data areprocessed to generate an input vector. In an embodiment, the monitoredparameter data from the wearable device 180 and the embedding dataobtained or derived from the health records is combined to generate aninput vector for an AI algorithm. In an embodiment, only a subset of themonitored parameter data is selected to be included in the input vector.For example, data points can be selected corresponding to a specifictime period (e.g., the most recent 6 months). As another example, onlydata points for a select number of monitored parameters (e.g., heartrate variability) are included in the input vector. In some embodiments,the data points are processed to generate derived data based on themonitored parameter data. For example, raw heart rate data can beprocessed to determine a maximum daily heart rate for each day over thelast 6 months. Similarly, the embedding data can also be processed toselect a subset of the embedding data to include in the input vector.

It will be appreciated that, in some cases, the health records for auser or the data points returned by the wearable device 180 may besparse. For example, the user may not visit a primary care physicianvery often or the user may forget to use the wearable device 180 on somedays. In some embodiments, where data is missing, the input vector maybe populated with zero values to substitute for missing elements of theinput vector. In other embodiments, the input vector may populatemissing elements with mean values for a population, a last logged valueprior to the missing data, or imputed values. For example, where a heartrate is missing for specific days, the input vector can be populatedwith a mean heart rate for a population or a subpopulation of users thatmatches the demographics of the particular user. The particular choiceof how to populate the input vector for missing values due to sparsedata is a design choice that can depend on the type of data that ismissing and/or the specific AI algorithm selected to process the inputvector.

In some embodiments, the input vector may also include additionalinformation related to the user registered to the wearable device. In anembodiment, demographic information for the user can be included in theinput vector. The demographic information, such as age, race, gender, orthe like, can affect the classifiers output by the algorithm wherecertain diseases disproportionately affect different demographicpopulations. In another embodiment, social determinants data can beincluded in the input vector. Social determinants data can includeeconomic information, neighborhood information, education information,nutritional information, or other types of environmental information.Examples of economic information can include employment data, income,expenses, debts, average medical bills, or the like. Examples ofneighborhood information can include zip code/geography based on addressof residence, types of housing, neighborhood characteristics (e.g.,number of parks, schools, average income, etc.). Examples of educationinformation can include level of schooling completed, literacy, primarylanguage, number of languages, and the like. Examples of nutritionalinformation can include measures related to access to food, dietaryintake, food availability, and the like. Examples of environmentalinformation can include stress, proximity to family (e.g., caregivers),access to healthcare, and the like.

The social determinants data can have a significant impact on healthoutcomes, but such information is rarely included in conventional healthrecords or tracked by a wearable device 180. In some embodiments, theuser will provide this information during interactions with the serviceprovider. For example, when opening an account with the serviceprovider, the user may fill in a form that provides at least some of theaforementioned social determinants data. In other embodiments, thesocial determinants data can be derived based on other information. Forexample, by receiving the user's address of residence, manycharacteristics related to the locality surrounding the address may beknown. For example, databases can be built that indicate, by zip code,the type or number of parks and schools in an area, the average incomein a given zip code, the location or number of health care providers ina certain area, and the like. Such generic information can be imputed ona user if specific information is not provided voluntarily.

In other instances, information related to a user can be derived fromthird-parties. For example, a user's credit history may provideinformation related to a user's financial stability and debts.Alternatively, the user may subscribe to other third-party serviceproviders that collect information that the user has opted-in andallowed to be shared with the service provider. For example, a serviceprovider that helps a user lose weight by tracking meals may shareinformation related to the user's diet with the service provider. Insome cases, such information has privacy concerns and, therefore, anopt-in by the user to share such information with the service providermay be required before such information can be retrieved from athird-party.

At step 508, the input vector is processed by the AI algorithm togenerate an output vector. In some embodiments, the AI algorithm is aMLP. In other embodiments, the AI algorithm is a CNN or an RNN. In anembodiment, the AI algorithm generates an output vector that includes aplurality of classifiers, each classifier is a scalar value thatrepresents a probability that the user has an underlying medicalcondition that may be undetected.

At step 510, a notification message is generated based on the outputvector. In an embodiment, each of the classifiers is compared against athreshold value. If the classifier is above the threshold value, thenthat may indicate a high likelihood that the user has a medicalcondition corresponding to that classifier. The notification message canbe sent to the user or a health care provider designated by the user toinform the user of the possibility of the medical condition.

In an embodiment, the notification message is transmitted to thewearable device to provide a user of the wearable device with asuggested action. The notification message can comprise a short messageservice (SMS) message, otherwise referred to as a text message, anapplication push notification, a message within an applicationtransmitted according to an API, an email, or the like. In oneembodiment, the text message can inform the user about the detection ofan underlying medical condition. In another embodiment, the suggestedaction includes information to facilitate scheduling an appointment witha healthcare provider. For example, when a new medical condition isdetected, the text message can suggest that the user schedule anappointment with the user's primary care provider and include a link tothe primary care provider's website, a listing of the primary careprovider's physical address, or a phone number to the primary careprovider's office.

In another embodiment, the notification message is transmitted to ahealth care provider to suggest treatment options that may be applicableto a patient. Given the underlying medical condition, the health careprovider can contact the patient and suggest treatment options or askthe patient to schedule an appointment. For certain conditions, it maybe preferred to have a trained medical professional (e.g., a doctor,nurse, therapist, psychologist, etc.) or a trained call representativecall the patient to inform them about the potential diagnosis in orderto answer any questions the patient might have concerning such diagnosisor the method for diagnosing the patient based on the monitoredparameter data, or to coordinate condition management by scheduling anappointment or the like with a medical professional.

FIG. 6 is a conceptual illustration of a health monitoring service 630for identifying medical conditions based on monitored parameter datacollected by a wearable device 180, in accordance with some embodiments.When a user obtains a wearable device 180, the user can opt-in to use ahealth monitoring service 630 offered by a service provider. In anembodiment, the user, through a client device 610, can access a web pageusing a web browser or other application executed by the client device610. In an embodiment, the web page is a portal for an insuranceprovider and the user is prompted to log into a user account usingcredentials established by the user for a user account. Once logged in,the user may select an option provided through the web page to registera wearable device with the account and opt-in to a health monitoringservice 630.

In one embodiment, the user provides information to identify thewearable device to the health monitoring service 630. For example, theuser can enter a MAC address of the wearable device in a form elementprovided on the webpage. In another example, the health monitoringservice 630 can prompt the user to download an application on thewearable device, where the application is configured to communicatedirectly with the health monitoring service 630. Once the application isrunning on the wearable device 630, the user can enter credentials forthe user account in the application on the wearable device 180 or,alternatively, the user can be provided with a temporary code at theclient device 610, which the user can then enter in the application onthe wearable device 180. The temporary code is then transmitted from thewearable device 180 to the health monitoring service 630, which can thenassociate an identifier corresponding to the wearable device 180 withthe user account based on the temporary code.

The example embodiments described above are merely a few of the manypossible ways to register the wearable device 180 to the user accountfor the user. Any means for registering the wearable device 180 to theuser account is contemplated as being within the scope of the presentdisclosure. Once the wearable device 180 is registered to the useraccount, such as by mapping a user account identifier to an identifierfor the wearable device 180 in a database 660, the monitored parameterdata received from the wearable device 180 can be associated with healthrecords corresponding to the user account in order to generate theembedding data utilized, at least in part, by the AI algorithm.

FIG. 7 illustrates an exemplary computer system 700, in accordance withsome embodiments. The computer system 700 includes a processor 702, anon-volatile memory 704, and a network interface controller (NIC) 720.The processor 702 can execute instructions that cause the computersystem 700 to implement the functionality various elements of the system100 described above. For example, the wearable device 180 and/or theserver device 110 can each take the form of the computer system 700.

Each of the components 702, 704, and 720 can be interconnected, forexample, using a system bus to enable communications between thecomponents. The processor 702 is capable of processing instructions forexecution within the system 700. The processor 702 can be asingle-threaded processor, a multi-threaded processor, a vectorprocessor or parallel processor that implements a single-instruction,multiple data (SIMD) architecture, or the like. The processor 702 iscapable of processing instructions stored in the volatile memory 704. Insome embodiments, the volatile memory 704 is a dynamic random accessmemory (DRAM). The instructions can be loaded into the volatile memory704 from a non-volatile storage, such as a Hard Disk Drive (HDD) or asolid state drive (not explicitly shown), or received via the network.In an embodiment, the volatile memory 704 can include instructions foran operating system 706 as well as one or more applications 708. It willbe appreciated that the application(s) can be configured to provide thefunctionality of one or more components of the system 100, as describedabove. The NIC 720 enables the computer system 700 to communicate withother devices over a network, including a local area network (LAN) or awide area network (WAN) such as the Internet.

It will be appreciated that the computer system 700 is merely oneexemplary computer architecture and that the processing devicesimplemented in the system 100 can include various modifications such asadditional components in lieu of or in addition to the components shownin FIG. 7. For example, in some embodiments, the computer system 700 canbe implemented as a system-on-chip (SoC) that includes a primaryintegrated circuit die containing one or more CPU cores, one or more GPUcores, a memory management unit, analog domain logic and the likecoupled to a volatile memory such as one or more SDRAM integratedcircuit dies stacked on top of the primary integrated circuit dies andconnected via wire bonds, micro ball arrays, and the like in a singlepackage (e.g., chip). In another embodiment, the computer system 700 canbe implemented as a server device, which can, in some embodiments,execute a hypervisor and one or more virtual machines that share thehardware resources of the server device.

It is noted that the techniques described herein may be embodied inexecutable instructions stored in a computer readable medium for use byor in connection with a processor-based instruction execution machine,system, apparatus, or device. It will be appreciated by those skilled inthe art that, for some embodiments, various types of computer-readablemedia can be included for storing data. As used herein, a“computer-readable medium” includes one or more of any suitable mediafor storing the executable instructions of a computer program such thatthe instruction execution machine, system, apparatus, or device may read(or fetch) the instructions from the computer-readable medium andexecute the instructions for carrying out the described embodiments.Suitable storage formats include one or more of an electronic, magnetic,optical, and electromagnetic format. A non-exhaustive list ofconventional exemplary computer-readable medium includes: a portablecomputer diskette; a random-access memory (RAM); a read-only memory(ROM); an erasable programmable read only memory (EPROM); a flash memorydevice; and optical storage devices, including a portable compact disc(CD), a portable digital video disc (DVD), and the like.

It should be understood that the arrangement of components illustratedin the attached Figures are for illustrative purposes and that otherarrangements are possible. For example, one or more of the elementsdescribed herein may be realized, in whole or in part, as an electronichardware component. Other elements may be implemented in software,hardware, or a combination of software and hardware. Moreover, some orall of these other elements may be combined, some may be omittedaltogether, and additional components may be added while still achievingthe functionality described herein. Thus, the subject matter describedherein may be embodied in many different variations, and all suchvariations are contemplated to be within the scope of the claims.

To facilitate an understanding of the subject matter described herein,many aspects are described in terms of sequences of actions. It will berecognized by those skilled in the art that the various actions may beperformed by specialized circuits or circuitry, by program instructionsbeing executed by one or more processors, or by a combination of both.The description herein of any sequence of actions is not intended toimply that the specific order described for performing that sequencemust be followed. All methods described herein may be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context.

The use of the terms “a” and “an” and “the” and similar references inthe context of describing the subject matter (particularly in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The use of the term “at least one” followed bya list of one or more items (for example, “at least one of A and B”) isto be construed to mean one item selected from the listed items (A or B)or any combination of two or more of the listed items (A and B), unlessotherwise indicated herein or clearly contradicted by context.Furthermore, the foregoing description is for the purpose ofillustration only, and not for the purpose of limitation, as the scopeof protection sought is defined by the claims as set forth hereinaftertogether with any equivalents thereof. The use of any and all examples,or exemplary language (e.g., “such as”) provided herein, is intendedmerely to better illustrate the subject matter and does not pose alimitation on the scope of the subject matter unless otherwise claimed.The use of the term “based on” and other like phrases indicating acondition for bringing about a result, both in the claims and in thewritten description, is not intended to foreclose any other conditionsthat bring about that result. No language in the specification should beconstrued as indicating any non-claimed element as essential to thepractice of the embodiments as claimed.

What is claimed is:
 1. A system for performing health monitoring, thesystem comprising: a server device including a memory and one or moreprocessors configured to: receive monitored parameter data from awearable device; obtain embedding data corresponding to a userregistered to the wearable device; process the monitored parameter dataand the embedding data to generate an input vector; process the inputvector by an artificial intelligence algorithm to generate an outputvector; and generate a notification message based on the output vector.2. The system of claim 1, wherein the wearable device is an activitytracker, and wherein the monitored parameter data includes data pointsrelated to one or more of: a heart rate; an oxygen level; an activitylevel including at least one of a number of steps, a number of flightsclimbed, or a duration of exercise; or a number of calories burned. 3.The system of claim 2, wherein the monitored parameter data furtherincludes information logged by a user manually.
 4. The system of claim1, wherein obtaining the embedding data comprises processing healthrecords for a user via a natural language processing algorithm, whereinthe health records correspond to a registered user of the wearabledevice.
 5. The system of claim 4, wherein the health records are storedin a database and comprise at least one of: claims records received froma health care provider; prescription records received from a pharmacy;or laboratory results received from a laboratory or other health careprovider.
 6. The system of claim 4, wherein the input vector datafurther includes social determinants data including one or more of:economic information; neighborhood information; education information;nutritional information; or other environmental information.
 7. Thesystem of claim 4, wherein the input vector data further includesdemographic data including one or more of: age information; genderinformation; neighborhood type information; family size information; oremployment indicator information.
 8. The system of claim 1, wherein theartificial intelligence algorithm comprises one or more of: amulti-layer perceptron (MLP) algorithm; a convolution neural network(CNN); or a recurrent neural network (RNN).
 9. The system of claim 1,wherein the notification message is transmitted to one of the wearabledevice or a mobile device associated with the wearable device to providea user of the wearable device with a suggested action.
 10. The system ofclaim 9, wherein the suggested action includes information to facilitatescheduling an appointment with a healthcare provider.
 11. The system ofclaim 1, wherein the notification message is transmitted to a healthcareprovider to suggest treatment options that may be applicable to apatient.
 12. A method for performing health monitoring, the methodcomprising: receiving monitored parameter data from a wearable device;obtaining embedding data corresponding to a user registered to thewearable device; processing the monitored parameter data and theembedding data to generate an input vector; processing the input vectorby an artificial intelligence algorithm to generate an output vector;and generating a notification message based on the output vector. 13.The method of claim 12, wherein the wearable device is an activitytracker, and wherein the monitored parameter data includes data pointsrelated to one or more of: a heart rate; an oxygen level; an activitylevel including at least one of a number of steps, a number of flightsclimbed, or a duration of exercise; or a number of calories burned. 14.The method of claim 12, wherein obtaining the embedding data comprisesprocessing health records for a user via a natural language processingalgorithm, wherein the health records correspond to a registered user ofthe wearable device.
 15. The method of claim 14, wherein the healthrecords are stored in a database and comprise at least one of: claimsrecords received from a health provider; prescription records receivedfrom a pharmacy; or laboratory results received from a laboratory orother health provider
 16. The method of claim 12, wherein the inputvector data further includes social determinants data including one ormore of: economic information; neighborhood information; educationinformation; nutritional information; or other environmentalinformation.
 17. The method of claim 12, wherein the input vector datafurther includes demographic data including one or more of: ageinformation; gender information; neighborhood type information; familysize information; or employment indicator information.
 18. The method ofclaim 12, wherein the notification message is transmitted to thewearable device to provide a user of the wearable device with asuggested action.
 19. The method of claim 12, wherein the notificationmessage is transmitted to a healthcare provider to suggest treatmentoptions that may be applicable to a patient.
 20. A wearable devicecomprising: a memory for storing data points associated with one or moremonitored parameters; a transceiver for communicating with a serverdevice over a network; and at least one processor configured to: sampleone or more sensors to generate data points for each of the one or moremonitored parameters; store the data points in the memory; transmit atleast a portion of the data points to the server device; and receive anotification message from the server device, wherein the notificationmessage includes a suggested action identified based on the output of anartificial intelligence algorithm configured to process an input vectorthat includes the at least a portion of the data points for the one ormore monitored parameters and embedding data corresponding to a userregistered to the wearable device.
 21. The wearable device of claim 20,wherein the one or more monitored parameters include at least one of: aheart rate; an oxygen level; an activity level including at least one ofa number of steps, a number of flights climbed, or a duration ofexercise; or a number of calories burned.
 22. The wearable device ofclaim 20, wherein the wearable device comprises a wearable activitytracker.